Decreasing lighting-induced false facial recognition

ABSTRACT

Photographing parameter(s) include a parameter(s) relating to exposure time and diaphragm, and is used upon capturing an image of a target. Before the image is obtained, lighting information is estimated based on the photographing parameter(s), and a number of feature points which is used upon facial recognition are determined based on the lighting information. The image is captured using the photographing parameter(s), and that performs, and facial recognition of the target is performed from the captured image using as many feature points as the determined number.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 14/647,305 filed on May 26, 2015, which is aNational Stage Entry of International Application PCT/JP2013/081885,filed on Nov. 27, 2013, which claims the benefit of priority fromJapanese Patent Application 2012-260049, filed on Nov. 28, 2012, thedisclosures of all of which are incorporated herein, in their entirety,by this reference.

TECHNICAL FIELD

The present invention relates to a facial recognition apparatus, arecognition method and a program therefor, and an information device.

BACKGROUND

In recent years, an identification by biological information such as aface, a fingerprint, an iris is used. Especially, since facialrecognition can be recognized with non-contact, and gives little load toa user, use of the facial recognition is expected to increase.

In Patent Literature 1, there is disclosed a technique to select a bestimage of a face for a facial recognition, after capturing an image ofthe face of a person by using single camera with high resolution and aplurality of cameras with low resolution. Especially, in the techniquedisclosed in Patent Literature 1, a region of a face is detected from aninput image with high resolution that is captured by the camera withhigh resolution, and brightness of the plurality of cameras with lowresolution is controlled based on distribution of pixel values of theregion of the detected face.

[Patent Literature 1]

Japanese Patent Kokai Publication No. 2009-134593A

SUMMARY

The disclosure of the above Patent Literature is incorporated herein byreference thereto. The following analysis has been given by the presentinvention.

When a facial recognition is performed, at first, it is desired toextract feature points (eyes, a nose, etc.) from an input imagecorrectly. Further, when a facial recognition is performed, it isdesired to extract a region of a face that is similar to an image of aface registered in a database.

Here, detection accuracy of feature points and recognition accuracy aredifferent depending on lighting environment. For example, when an imageof a face is captured indoors without light, the image of a face is notcaptured clearly, which may cause false detection of the feature points.And, when an image of a face is captured outdoors with sunlight,reflection of sunlight may cause false detection of the feature points.

In the technique disclosed in Patent Literature 1, decrease of detectionaccuracy and recognition accuracy of the feature points due to thelighting environment is not considered.

There is a need in the art to contribute to decreasing false recognitiondue to the lighting environment.

According to a first aspect, there is provided a facial recognitionapparatus, comprising: a photographing parameter input unit thatreceives a photographing parameter(s); a lighting information estimationunit that estimates lighting information based on the photographingparameter(s); and a recognition accuracy control unit that controls arecognition accuracy parameter(s) based on the lighting information.

According to a second aspect, there is provided a recognition method,comprising: receiving a photographing parameter(s); estimating lightinginformation based on the photographing parameter(s); and controlling arecognition accuracy parameter(s) based on the lighting information.

According to a third aspect, there is provided a program causing acomputer for controlling a facial recognition apparatus to execute:receiving a photographing parameter(s); estimating lighting informationbased on the photographing parameter(s); and controlling a recognitionaccuracy parameter(s) based on the lighting information.

This program can be recorded in a computer-readable non-transientstorage medium. Namely, the present invention can be embodied as acomputer program product.

According to a fourth aspect, there is provided an information devicecomprising a facial recognition apparatus, wherein the facialrecognition apparatus comprises a photographing parameter input unitthat receives a photographing parameter(s); a lighting informationestimation unit that estimates lighting information based on thephotographing parameter(s); and a recognition accuracy control unit thatcontrols a recognition accuracy parameter(s) based on the lightinginformation

According to each aspect of the present invention, a facial recognitionapparatus, a recognition method and a program therefor, and aninformation device contributing to decreasing false recognition due tothe lighting environment are provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B are drawings for explaining an exemplary embodiment.

FIG. 2 is a block diagram of an example of an internal configuration ofa facial recognition apparatus 1 relating to an exemplary embodiment 1.

FIG. 3 is a block diagram of an example of an internal configuration ofa photographing apparatus 20 relating to the exemplary embodiment 1.

FIG. 4 is a drawing of an example of a table of showing a relationshipbetween an illuminance and a FAR.

FIG. 5 is a drawing of an example of a function of showing arelationship between the illuminance and FAR.

FIG. 6 is a flowchart of an example of processes of controlling therecognition accuracy.

FIG. 7 is a plan image of an example of showing the overallconfiguration of an information device 2 relating to an exemplaryembodiment 2.

FIG. 8 is a block diagram of an example of an internal configuration ofthe information device 2 relating to the present exemplary embodiment

PREFERRED MODES

First, an outline of an exemplary embodiment of the present inventionwill be described with reference to the drawings. In the followingoutline, various components are denoted by reference characters for thesake of convenience. Namely, the following reference characters aremerely used as examples to facilitate understanding of the presentinvention, not to limit the present invention to the illustrated modes.

As described above, when a facial recognition is performed, there is acase where an accuracy of the facial recognition decreases depending onlighting environment. Therefore, depending on the lighting environment,it is desired a facial recognition apparatus to contribute to decreasingfalse recognition.

A facial recognition apparatus 100 shown in FIG. 1A and FIG. 1B areprovided as an example. A facial recognition apparatus 100 comprises aphotographing parameter input unit 101 that receives a photographing(imaging) parameter(s); a lighting information estimation unit 102 thatestimates lighting information based on the photographing parameter(s);and a recognition accuracy control unit 103 that controls a recognitionaccuracy parameter(s) based on the lighting information.

The facial recognition apparatus 100 receives a photographingparameter(s) (step S1001). The photographing (imaging) parameter(s)means a parameter(s) that is set as a photographing (imaging) conditionwhen a photographing (imaging) apparatus (camera) captures an image of atarget. Especially, it is preferred that the photographing parameter(s)includes a parameter(s) relating to a gain, exposure time, a diaphragm,a brightness of a target, etc. And, the facial recognition apparatus 100estimates the lighting information based on the photographingparameter(s) (step S1002). The lighting information means informationthat indicates brightness at target's neighborhood (a lightingenvironment). And, the facial recognition apparatus 100 controls therecognition accuracy parameter(s) based on the lighting information(step S1003). Therefore, the facial recognition apparatus minimizes thatrecognition accuracy decreases depending on the lighting informationwhen a facial recognition is performed. Hence, the facial recognitionapparatus 100 contributes to decreasing false recognition depending onthe lighting information.

Concrete exemplary embodiments will be described below in more detailwith reference to the drawings.

Exemplary Embodiment 1

An exemplary embodiment 1 will be described in more detail withreference to the drawings.

FIG. 2 is a block diagram of an example of an internal configuration ofa facial recognition 1 of the present exemplary embodiment. The facialrecognition apparatus comprises an image capture unit 11, a facialrecognition unit 12, a photographing parameter input unit 13, a lightinginformation estimation unit 14, a recognition accuracy control unit 15,a recognition accuracy management database 16, and face image database17. And, the facial recognition apparatus 1 is connected with aphotographing apparatus 20. For simplicity, FIG. 2 only shows modulesrelevant to the facial recognition apparatus 1 relating to the presentexemplary embodiment.

First, the facial recognition apparatus will be described in detail.

The photographing apparatus 20 captures an image of a target.Concretely, the photographing apparatus 20 captures an image of a targetbased on a predetermined photographing parameter(s).

FIG. 3 is a block diagram of an example of an internal configuration ofthe photographing apparatus 20. The photographing apparatus 20 comprisesa photographing lens 21, an image sensor 22, a photographing parameterrecord unit 23, and a photographing control unit 24. For simplicity,FIG. 3 only shows modules relevant to the photographing apparatus 20relating to the present exemplary embodiment.

The photographing lens 21 is configured with a plurality of opticalsystems including a zoom lens and a focus lens. For simplicity, FIG. 3shows the photographing lens 21 as a single lens.

For example, the image sensor 22 is configured with a CCD (ChargeCoupled Device), a CMOS (Complementary Metal Oxide Semiconductor), etc.A light signal collected by the photographing lens 21 makes an image ona surface of the image sensor 22 that receives light. And, the imagesensor 22 transforms the received light signal to an electric signal (ananalog signal).

The photographing parameter record unit 23 records a photographingparameter(s). Concretely, the photographing parameter record unit 23records the photographing parameter(s) such as a gain, exposure time, adiaphragm, a brightness of a target, etc.

The photographing control unit 24 controls the whole of thephotographing apparatus 20, and each module shown in FIG. 3. Concretely,the photographing control unit 24 controls a process of photographingbased on the photographing parameter(s). Further, the photographingcontrol unit 24 can be embodied by a computer program causing a computermounted on the photographing apparatus 20 to execute processes of thephotographing apparatus 20 using the hardware of the computer.

Next, the facial recognition apparatus 1 will be described in detail.

The face image database 17 records a face image(es) of one or moreperson. Further, the face image database 17 may record the face imagewith a plurality of face angles for each person. Note that, in thefollowing description, an image registered in the face image database 17is referred to as a template image. And, the face image database 17 mayextract a feature point(s) from a template image in advance, and recordthem.

The image capture unit 11 captures an image that the photographingapparatus 20 captures. Note that, in the following description, an imagethat the photographing apparatus 20 captures is referred to as arecognition target image. It is preferred that the recognition targetimage includes a face region.

The facial recognition unit 12 collates a recognition target image witha template image, and recognizes. Concretely, the facial recognitionunit 12 extracts a feature point(s) from the recognition target imageand the template image. For example, it is preferred that the facialrecognition unit 12 extracts an edge point(s) of eyes, a mouse, a nose,etc. as the feature point(s). Further, there are various methods forrecognizing a feature point(s) of a face and the face, any method forrecognizing them can be used.

The photographing parameter unit 13 receives the photographingparameter(s). Concretely, the photographing parameter input unit 13refers to the photographing parameter record unit 23, and obtains thephotographing parameter(s). Namely, the photographing parameter inputunit 13 obtains the photographing parameter(s) such as a gain, exposuretime, a diaphragm, etc. used when the photographing apparatus 20 hascaptured an image of a face.

The lighting information estimation unit 14 estimates the lightinginformation based on the photographing parameter(s). For example, thelighting information estimation unit 14 may estimate an illuminance(unit: lx, lux) based on the photographing parameter(s).

For example, the lighting information estimation unit 14 may estimatethe illuminance based in the following equation (1). Further, this doesnot aim to limit a method of estimation of the illuminance to theequation (1).

[Equation 1]

$\begin{matrix}{E = \frac{\gamma \times F^{2} \times \left( {1 + M} \right)^{2}}{T \times {ISO}}} & (1)\end{matrix}$E: IlluminanceF: DiaphragmM: MagnificationT: Exposure TimeISO: Sensitivityγ: Constant

Further, γ is different according to an image sensor. For example, γ mayrange from 200 to 235.

The recognition accuracy control unit 15 controls a recognition accuracyparameter(s) based on the lighting information. Namely, the recognitionaccuracy control unit 15 controls a recognition accuracy based onrelationship between lighting information and the recognition accuracyparameter(s). Here, the recognition accuracy control parameter(s) meansa parameter(s) that influences recognition accuracy. In the followingdescription, note that a rate to accept a wrong person in fail isreferred to as a FAR (False Acceptance Rate). And, note that a rate toreject a correct person is referred to as a FRR (False Rejection Rate).It is preferable for FAR and FRR to decrease as the recognition accuracyincreases.

In the following, it shows an example of the recognition accuracycontrol parameter(s). But, the following explanation does not aim tolimit the recognition accuracy control parameter(s) to the followingexample.

For example, the recognition accuracy control parameter(s) may be thenumber of the feature points to be used for facial recognition. Namely,the recognition accuracy control unit 15 may control the number of thefeature points to be used for facial recognition based on the lightinginformation. Here, the recognition accuracy may increase as the numberof feature points increase. Therefore, the recognition accuracy controlunit 15 can control the recognition accuracy by changing the number offeature points.

And, the recognition accuracy control parameter(s) may a weight(s) forthe feature point(s). Namely, the recognition accuracy control unit 15may control the weight(s) for the feature point(s) based on the lightinginformation. In this case, it is preferred that the recognition accuracycontrol unit 15 changes the weight(s) for the feature point(s) based onsimilarities between the feature point(s) of a template image and thefeature point(s) of a recognition target image. As the feature pointswith low similarity increase, the possibility of false recognitionincreases. Therefore, the recognition accuracy control unit 15 cancontrol the recognition accuracy by changing the weight(s) for thefeature points.

And, the recognition accuracy control parameter(s) may be a threshold ofan evaluation value. Namely, when the facial recognition unit 12determines a result by whether or not an evaluation value is more than apredetermined threshold, the recognition accuracy control unit 15 maycontrol the threshold of the evaluation value based on the lightinginformation. It is preferred that the evaluation value is a calculatedvalue based on similarities of each feature point. In this case, as thethreshold of the evaluation value increases, the possibility of falserecognition decreases. Therefore, the recognition accuracy control unit15 can control by changing the threshold of the evaluation value.

For example, regarding the each feature point, the facial recognitionunit 12 calculates similarities between the recognition target image andthe template images. And, the facial recognition unit 12 may set theaccumulated value of the calculated similarities as the evaluationvalue. There are various calculation methods of the evaluation value,but any calculation method of the evaluation value can be used.

And, for each person, the face image database 17 may record his/her faceimages with a plurality of face angles. In this case, the recognitionaccuracy control unit 15 may control a number of patterns of face anglesof template images. By changing the face angles of the template images,the difference from other person sometimes becomes clear. Therefore, therecognition accuracy control unit 15 can control the recognitionaccuracy by changing the number of the patterns of face angles of thetemplate images.

The recognition accuracy management database 16 stores the lightinginformation and the recognition accuracy parameter(s) in association.For example, the recognition accuracy management database 16 may storean illuminance in a predetermined range and a predetermined recognitionaccuracy parameter(s) in association. Namely, the recognition accuracymanagement database 16 may record a table associated the lightinginformation with the recognition accuracy (including FAR, FRR, etc.).Or, the recognition accuracy management database 16 may record afunction associated the lighting information with the recognitionaccuracy.

The recognition accuracy management database 16 may record arelationship between the lighting information and the recognitionaccuracy control parameter(s). For example, the recognition accuracymanagement database 16 may record a relationship between the lightinginformation and a number of the feature points. The recognition accuracymanagement database 16 may record a relationship between the lightinginformation and a weight(s) for the feature point(s). The recognitionaccuracy management database 16 may record a relationship between thelighting information and a threshold(s) of the evaluation values. Therecognition accuracy management database 16 may record a relationshipbetween the lighting information and a FAR. Further, in this case, it ispreferred the recognition accuracy management database 16 records thenumber of the feature points, the thresholds(s) of the evaluationvalues, etc. associating with a FAR.

FIG. 4 is a drawing of an example of a table of showing a relationshipbetween an illuminance and a FAR. For example, when a face is recognizedindoors without light, it may be difficult for the facial recognition 12to extract the feature point(s). Namely, when a face is recognizedindoors without light, it is possible that the facial recognition unit12 cannot recognize a face correctly.

Therefore, when a face is recognized indoors without light, it ispreferred that the recognition accuracy control unit 15 sets therecognition accuracy to be lower than when a face is recognized indoorswith light. Concretely, it is preferred that the recognition accuracycontrol unit 15 controls the recognition accuracy control parameter(s)such that a FAR is higher, when a face is recognized indoors withoutlight. Namely, it is preferred that controls the recognition accuracycontrol parameter(s) such that a recognition accuracy is lower (a FAR ishigher), when a face is recognized indoors without light.

On the other hand, it is possible that the facial recognition unit 12can extract the feature point(s) easily, when a face is recognized in acloudy outdoor environment. However, it is possible that the facialrecognition unit 12 recognize a wrong person in fault, when a face isrecognized in the cloudy outdoor environment. Namely, it is assumed thatit tends that a FAR is higher, when a face is recognized in the cloudyoutdoor environment.

Therefore, as shown in FIG. 4, in the case where a face is recognized inthe cloudy outdoor environment, it is preferred that the recognitionaccuracy control unit 15 makes a FAR be lower than when a face isrecognized outdoors with backlight and direct light. Concretely, in thecase where a face is recognized in the cloudy outdoor environment, it ispreferred that the recognition accuracy control unit 15 controls therecognition accuracy control parameter(s), such that a FAR is lower.

FIG. 5 is a drawing of an example of a function of showing arelationship between an illuminance and a FAR. As shown in FIG. 5, therecognition accuracy management database 16 may record the relationshipbetween the illuminance and the FAR such that the FAR changescontinuously, according to the illuminance.

Next, an operation of the facial recognition apparatus 1 relating to thepresent exemplary embodiment will be described.

FIG. 6 is a flowchart of an example of processes of controlling therecognition accuracy.

In step S1, the photographing parameter input unit 13 receives thephotographing parameter(s). Concretely, it is preferred that thephotographing parameter input unit 13 obtains the photographingparameter(s) form the photographing apparatus 20.

In step S2, the lighting information estimates the lighting informationbased on the photographing parameter(s).

In step S3, the recognition accuracy control unit 15 determines therecognition accuracy based on the lighting information.

Concretely, the recognition accuracy control unit 15 refers to therecognition accuracy management database 16, and determines therecognition accuracy parameter(s) based on the lighting information.

In step S4, the image capture unit 11 obtains the recognition targetimage form the photographing apparatus 20. And, the facial recognitionunit 12 determines whether or not a face region is included in therecognition target image. For example, the facial recognition unit 12detects feature points of eyes (for example, edge points of eyes, etc.)from the recognition target image. And, the facial recognition unit 12may determine that the face region is included in the recognition targetimage, when the feature points of eyes are detected.

When the face region is included in the recognition target image (Yes inthe step S5), the facial recognition unit 12 extracts the face regionfrom the recognition target image (step S6). For example, the facialrecognition unit 12 may extract the face region based on the positionsof eyes. On the other hand, when the face region is not included in therecognition target image (No in the step S5), the process of controllingthe recognition accuracy will finish.

In step S7, the facial recognition unit 12 recognizes a face based onthe recognition accuracy. Concretely, the facial recognition unit 12sets the recognition accuracy parameter(s) based on the recognitionaccuracy. And, the facial recognition unit 12 may recognize a face usingthe recognition accuracy parameter(s) that is set.

Further, the recognition accuracy control unit 15 may control therecognition accuracy parameter(s) based on the photographingparameter(s). Namely, the recognition accuracy control unit 15 maycontrol the recognition accuracy based on the relationship between thephotographing parameter(s) and the recognition accuracy parameter(s).And, the recognition accuracy management database 16 may record therelationship between the photographing parameter(s) and the recognitionaccuracy control parameter(s).

For example, the recognition accuracy control unit 15 may control anumber of the feature points used for recognition based on thephotographing parameter(s). Or, the recognition accuracy control unit 15may control a weight(s) for the feature point(s) based on thephotographing parameter(s). Or the recognition accuracy control unit 15may control a number of patterns of face angles of the template images.

[Modification 1]

As a modification 1 relating to the exemplary embodiment 1, an apparatusthat is (in the following, the apparatus is referred to as a serverapparatus) different from the facial recognition may comprise the facialrecognition unit 12 and the face image database 17. Because, in thefacial recognition unit 12, a load of the process of the recognizingchanges depending on a number of the template images. Therefore, theserver apparatus that has higher performance that the facial recognitionapparatus 1 may comprise the facial recognition unit 12 and the faceimage database 17. Further, in this case, the facial recognition 1 andthe server apparatus may control via a network.[Modification 2]As a modification 2 relating the exemplary embodiment 1, thephotographing apparatus 20 may comprises the illuminance sensor. In thiscase, the photographing parameter input unit receives an output value ofthe illuminance sensor as the photographing parameter. And, theilluminance information estimation unit 14 may estimate the illuminanceinformation based on the output value of the illuminance sensor.

A first effect of the facial recognition apparatus relating to thepresent exemplary embodiment is to decrease false recognition. Forexample, when a face is recognized in an environment such as indoorwithout light etc., it may be false recognition. However, the facialrecognition apparatus 1 relating to the present exemplary embodimentcontrols the recognition accuracy based on the illuminance informationsuch as to decrease the false recognition. Therefore, the facialrecognition apparatus 1 relating to the exemplary embodiment contributesto decreasing false recognition depending on the lighting information.

A second effect of the facial recognition apparatus 1 relating to thepresent exemplary embodiment is to decrease a user's load for the facialrecognition. In the technique disclosed in Patent Literature 1, when thefacial recognition has been failed, it is necessary to capture an imageagain. However, the facial recognition apparatus 1 relating to thepresent exemplary embodiment controls the recognition accuracy todecrease a possibility of false recognition. Therefore, the facialrecognition apparatus 1 relating to the present exemplary embodimentdoes not need to capture a face image repeatedly. Hence, the facialrecognition apparatus 1 relating to the present exemplary embodimentcontributes to decreasing user's load.

Exemplary Embodiment 2

In the followings, the exemplary embodiment 2 will be described in moredetail with reference to the drawings.

The present exemplary embodiment is an embodiment where an informationdevice comprises a facial recognition apparatus. Note that thedescription that overlaps with the exemplary embodiment described abovewill be omitted in the description of the present exemplary embodiment.Further, the same signs are given to the elements same as those in theexemplary embodiment described above and the explanation thereof will beomitted in the description of the present exemplary embodiment.

FIG. 7 is a plan image of an example of showing the overallconfiguration of an information device 2 relating to the presentexemplary embodiment. The information device 2 comprises thephotographing apparatus 20, a display apparatus 30 and an operation unit40. Note that, FIG. 7 does not aim to limit the information device 2relating to the present apparatus to an embodiment shown in FIG. 7. Forexample, the information device 2 may be an information device such as asmartphone, a mobile telephone, a game device, a tablet PC (PersonalComputer), a note PC, a PDA (Personal Data Assistants), a digitalcamera, etc.

The photographing apparatus 20 can capture an image of a user's facethat is facing to the display unit 30. The photographing apparatus 20may have feature as an in-camera of the information device 2.

By the display unit, a user visually recognizes information (characters,pictures, etc.) that the information device 2 shows. As the display unit30, a liquid crystal panel, an electro luminescence panel, etc. may beused.

The operation unit 40 receives user's operation for the informationdevice 2. While FIG. 7 shows hardware keys as the operation unit 40, anoperation means such as a touch panel, etc. may be adopted.

FIG. 8 is a block diagram of an example of an internal configuration ofthe information device 2 relating to the present exemplary embodiment.The information device 2 comprises the image capture unit 11, the facialrecognition unit 12, the photographing parameter input unit 13, thelighting information estimation unit 14, the recognition accuracycontrol unit 15, the recognition accuracy management database 16, theface image database 17, the photographing apparatus 20, the display unit30, the operation unit 40, and an information device control unit 50.Namely, the information device 2 comprises the facial recognitionapparatus 1. Note that, for simplicity, FIG. 8 only shows modulesrelevant to the information device 2 relating to the present exemplaryembodiment.

The information device control unit 50 controls the whole of theinformation device 2, and modules shown in FIG. 7. The informationdevice control unit 50 can be embodied by a computer program causing acomputer mounted on the information device 2 to execute processes of theinformation device 2 using the hardware of the computer.

For example, the information device control unit 50 may display on thedisplay unit 30 a result of recognition by the facial recognition unit12. And, based on the operation to the operation unit 40, theinformation device control unit 50 may determine whether or not thefacial recognition unit 12 starts a facial recognition. Alternatively,based on the result of the recognition by the facial recognition unit12, the information device control unit 50 may determine whether or notthe user's operation to the operation unit 40 is accepted.

As described above, the information device 2 relating to the presentexemplary embodiment comprises functions of the facial recognitionapparatus 1. And, the information device relating to the exemplaryembodiment controls processes to be executed based on a result ofrecognition of a face. Namely, the information device relating to thepresent exemplary embodiment comprises functions for high security.Therefore, the information device 2 relating to the present exemplaryembodiment contributes to decreasing false recognition, and providingfunctions for high security.

Exemplary Embodiment 3

In the following, the exemplary embodiment 3 will be described in moredetail.

The present exemplary embodiment is an embodiment of an informationdevice that executes an application that has a facial recognitionfunction. Note that the description that overlaps with the exemplaryembodiment describe above will be omitted in the description of thepresent exemplary embodiment. Further, the same signs are given to theelements same as those in the exemplary embodiment described above andthe explanation thereof will be omitted in the description of thepresent exemplary embodiment.

The information device 2 relating to the present exemplary embodimentcontrols applications. And, the information device control unit 50controls the application that has a facial recognition function, basedon a result of facial recognition.

For example, the application that has a facial recognition function mayexecute an application that resets a screen lock, when a face isrecognized. Here, the screen lock means a state where a user's operationto the operation unit 40 cannot be accepted. Further, there are variousapplications that have the facial recognition function, but anyapplication that has the facial recognition function can be used.

[Modification 1]

As a modification 1 of the information device 2 relating to the presentexemplary embodiment, recognition accuracy may change according to theapplication. For example, let's assume that the information device 2mounts an alarm application and an electronic money managementapplication as the application that has the facial recognition function.In this case, the recognition accuracy control unit 15 may set therecognition accuracy to be higher for the electronic money managementapplication than that for the alarm application.

As described above, the information device 2 relating to the presentexemplary embodiment mounts the application that has the facialrecognition function. And, when a face is recognized, the informationdevice 2 relating to the present exemplary embodiment allows that a useruses the application. Therefore, the information device 2 relating tothe present exemplary embodiment contributes more to providing functionsfor high security.

A part of/a whole of the above exemplary embodiment can be described asthe following modes, but not limited to the following modes.

(Mode 1)

As the facial recognition apparatus relating to the first aspect.

(Mode 2)

The facial recognition apparatus according to Mode 1, wherein therecognition accuracy control unit controls a threshold to determine aresult of recognition based on the lighting information.

(Mode 3)

The facial recognition apparatus according to Mode 1 or 2, wherein therecognition accuracy control unit controls a feature point(s) to becollated, based on the lighting information.

(Mode 4)

The facial recognition apparatus according to Mode 3, wherein therecognition accuracy control unit controls a weight(s) for the featurepoint(s) based on the lighting information.

(Mode 5)

The facial recognition apparatus according to any one of Modes 1 to 4,comprising a recognition accuracy management database that stores thelighting information and the recognition accuracy parameter(s) inassociation.

(Mode 6)

The facial recognition apparatus according to any one of Modes 1 to 5,wherein the recognition accuracy control unit controls the recognitionaccuracy parameter(s) based on the photographing parameter(s) instead ofthe lighting information.

(Mode 7)

An information device, comprising the facial recognition apparatusaccording to any one of Modes 1 to 6.

(Mode 8)

The information device according to Mode 7, wherein the informationdevice executes an application that has a facial recognition function.

(Mode 9)

As the recognition method relating to the second aspect.

(Mode 10)

The recognition method according to Mode 9, controlling a threshold todetermine a result of recognition based on the lighting information.

(Mode 11)

The recognition method according to Mode 9 or 10, controlling a featurepoint(s) to be collated, based on the lighting information.

(Mode 12)

The recognition method according to Mode 11, controlling a weight(s) fora feature point(s) based on the lighting information.

(Mode 13)

The recognition method according to any one of Modes 9 to 12,controlling the recognition accuracy parameter(s) based on thephotographing parameter(s) instead of the lighting information.

(Mode 14)

As the program relating to the third aspect.

(Mode 15)

The program according to Mode 14, controlling a threshold to determine aresult of recognition based on the lighting information.

(Mode 16)

The program according to Mode 14 or 15, controlling a feature point(s)to be collated, based on the lighting information.

(Mode 17)

The program according to Mode 16, controlling a weight(s) for thefeature point(s) based on the lighting information.

(Mode 18)

The program according to any one of Modes 14 to 17, controlling therecognition accuracy parameter(s) based on the photographingparameter(s) instead of the lighting information

The disclosure of the above Patent Literature is incorporated herein byreference thereto. Modifications and adjustments of the exemplaryembodiments and examples are possible within the scope of the overalldisclosure (including the claims) of the present invention and based onthe basic technical concept of the present invention. Variouscombinations and selections of various disclosed elements (includingeach element in each claim, exemplary embodiment, example, drawing,etc.) are possible within the scope of the claims of the presentinvention. Namely, the present invention of course includes variousvariations and modifications that could be made by those skilled in theart according to the overall disclosure including the claims and thetechnical concept.

-   1, 100 facial recognition apparatus-   2 information device-   11 image capture unit-   12 facial recognition unit-   13, 101 photographing parameter input unit-   14, 102 lighting information estimating unit-   15, 103 recognition accuracy control unit-   16 recognition accuracy management database-   17 face image database-   20 photographing apparatus-   21 photographing lens-   22 image sensor-   23 photographing parameter record unit-   24 photographing control unit-   30 display unit-   40 operation unit-   50 information device control unit

The invention claimed is:
 1. A facial recognition apparatus connected toa photographing apparatus that captures an image of a target,comprising: hardware, including a processor and memory; a photographingparameter input unit implemented at least by the hardware and thatreceives a photographing parameter(s), wherein the photographingparameter(s) includes a parameter(s) relating to exposure time anddiaphragm, and is used upon capturing, on the photographing apparatus,the image of the target; a lighting information estimation unitimplemented at least by the hardware and that estimates, beforeobtaining the image of the target from the photographing apparatus,lighting information based on the photographing parameter(s); arecognition accuracy control unit implemented at least by the hardwareand that determines, before obtaining the image of the target from thephotographing apparatus, a number of feature points which are used uponfacial recognition, based on the lighting information; and a recognitionunit implemented at least by the hardware to cause the photographingapparatus to capture the image of the target using the photographingparameter(s), and that performs, after obtaining the image of the targetfrom the photographing apparatus, the facial recognition of the targetfrom the captured image using as many feature points as the determinednumber.
 2. The facial recognition apparatus according to claim 1,comprising a recognition accuracy management database that stores thelighting information and the number of feature points which are usedupon facial recognition in association, wherein recognition accuracycontrol unit refers to the recognition accuracy management database todetermine the number of feature points.
 3. The facial recognitionapparatus according to claim 1, comprising a control unit implemented atleast by the hardware to control, based on a result of the facialrecognition, an application that has a facial recognition function. 4.The facial recognition apparatus according to claim 1, comprising thephotographing apparatus.
 5. A recognition method, comprising: receivinga photographing parameter(s), wherein the photographing parameter(s)includes a parameter(s) relating to exposure time and diaphragm, and isused upon capturing, on a photographing apparatus, an image of a target;estimating, before obtaining the image of the target, lightinginformation based on the photographing parameter(s); and determining,before obtaining the image of the target, a number of feature pointswhich are used upon facial recognition, based on the lightinginformation; obtaining the image of the target from the photographingapparatus that captures the image of the target using the photographingparameter(s); and performing, after obtaining the image of the targetfrom the photographing apparatus, the facial recognition of the targetfrom the captured image using as many feature points as the determinednumber.
 6. The recognition method according to claim 5, wherein thedetermining comprises referring to a recognition accuracy managementdatabase that stores the lighting information and the number of featurepoints which are used upon facial recognition in association.
 7. Therecognition method according to claim 5, further comprising controlling,based on a result of the facial recognition, an application that has afacial recognition function.
 8. A non-transitory computer readablerecording medium storing a program that causes a computer forcontrolling a facial recognition apparatus to execute, wherein thefacial recognition apparatus connects to a photographing apparatus thatcaptures an image of a target: receiving a photographing parameter(s),wherein the photographing parameter(s) includes a parameter(s) relatingto exposure time and diaphragm, and is used upon capturing, on aphotographing apparatus, an image of a target; estimating, beforeobtaining the image of the target, lighting information based on thephotographing parameter(s); and determining, before obtaining the imageof the target, a number of feature points which are used upon facialrecognition, based on the lighting information; obtaining the image ofthe target from the photographing apparatus using the photographingparameter(s); and performing, after obtaining the image of the targetfrom the photographing apparatus, recognition of the target from thecaptured image using as many feature points as the determined number. 9.The recording medium according to claim 8, wherein the determiningcomprises referring to a recognition accuracy management database thatstores the lighting information and the number of feature points whichare used upon facial recognition in association.
 10. The recordingmedium according to claim 8, wherein the program further causes thecomputer to execute controlling, based on a result of the facialrecognition, an application that has a facial recognition function.